Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR025728 |
On the Optimal Spatial Design for Groundwater Level Monitoring Networks | |
Ohmer, M.; Liesch, T.; Goldscheider, N. | |
2019-11-19 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
文章类型 | Article;Early Access |
语种 | 英语 |
国家 | Germany |
英文摘要 | Effective groundwater monitoring networks are important, as systematic data collected at observation wells provide a crucial understanding of the dynamics of hydrogeological systems as well as the basis for many other applications. This study investigates the influence of six groundwater level monitoring network (GLMN) sampling designs (random, grid, spatial coverage, and geostatistical) with varying densities on the accuracy of spatially interpolated groundwater surfaces. To obtain spatially continuous prediction errors (in contrast to point cross-validation errors), we used nine potentiometric groundwater surfaces from three regional MODFLOW groundwater flow models with different resolutions as a priori references. To assess the suitability of frequently-used cross-validation error statistics (MAE, RMSE, RMSSE, ASE, and NSE), we compared them with the actual prediction errors (APE). Additionally, we defined upper and lower thresholds for an appropriate spatial density of monitoring wells. Below the lower threshold, the observation density appears insufficient, and additional wells lead to a significant improvement of the results. Above the upper threshold, additional wells lead to only minor and inefficient improvements. According to the APE, systematic sampling lead to the best results but is often not suited for GLMN due to its nonprogressive characteristic. Geostatistical and spatial coverage sampling are considerable alternatives, which are in contrast progressive and allow evenly spaced and, in the case of spatial coverage sampling, yet reproducible coverage with accurate results. We found that the global cross-validation error statistics are not suitable to compare the performance of different sampling designs, although they allow rough conclusions about the quality of the GLMN. |
英文关键词 | groundwater level monitoring network sampling design low-discrepancy geostatistics spatial optimization |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000498319100001 |
WOS关键词 | INTERPOLATION METHODS ; MULTIOBJECTIVE OPTIMIZATION ; SAMPLING STRATEGIES ; GENETIC ALGORITHM ; ENTROPY ; GIS ; METHODOLOGY ; MORPHOLOGY |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/223916 |
专题 | 资源环境科学 |
作者单位 | Karlsruhe Inst Technol, Div Hydrogeol, Inst Appl Geosci, Karlsruhe, Germany |
推荐引用方式 GB/T 7714 | Ohmer, M.,Liesch, T.,Goldscheider, N.. On the Optimal Spatial Design for Groundwater Level Monitoring Networks[J]. WATER RESOURCES RESEARCH,2019. |
APA | Ohmer, M.,Liesch, T.,&Goldscheider, N..(2019).On the Optimal Spatial Design for Groundwater Level Monitoring Networks.WATER RESOURCES RESEARCH. |
MLA | Ohmer, M.,et al."On the Optimal Spatial Design for Groundwater Level Monitoring Networks".WATER RESOURCES RESEARCH (2019). |
条目包含的文件 | 条目无相关文件。 |
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